Numerous pathologies have different histologic phenotypes but similar radiographic appearances. In particular, cancer subtypes often have different histologic phenotypes but similar radiographic appearances. These similar radiographic appearances may lead to difficulties in differentiating the different subtypes in a clinical environment. For example, fibroadenoma (FA), a benign breast tumor, and triple negative (TN), an aggressive breast cancer, have similar morphological appearances when viewed with magnetic resonance imaging (MRI) but have distinct cellular and architectural arrangements when evaluated on a pathology slide under a microscope. Similarly, radiation necrosis (RN) is difficult to distinguish from recurrent brain tumors (rBT) for primary and metastatic brain tumors when viewed with MRI.
Conventional methods for characterizing and distinguishing subtly different pathologies have employed analysis of texture features. However, conventional methods tend to capture global textural patterns. One conventional method that captures global textural patterns employs grey-level co-occurrence matrices (GLCM) and Gabor steerable features to compute global relationships between pixels by averaging responses to various filter operators within a neighborhood to a single global descriptor.
Another conventional approach to distinguishing subtly different pathologies employs local binary patterns (LBP) to provide a pixel-level response that can be used to generate a pixel-level or patch-based classification. Unlike GLCM, LBP provides a signature for every pixel by capturing localized intensity variations across the pixel. However, LBP is highly dependent on the radius parameter, which is critical when extracting local patterns. Additionally, both global and per-pixel texture representations are based on intensity variations and are domain agnostic. However, the histopathological differences between subtly different classes may be manifested in differently oriented nuclei, lymphocytes, and glands. These differences in histopathological architecture, which are reflected in MRI imaging, are not reliably captured on a local scale by conventional methods.